---
title: "DASHBOARD"
output:
flexdashboard::flex_dashboard:
orientation: rows
vertical_layout: fill
social: [ "twitter", "facebook","instagram", "menu"]
source_code: embed
---
```{r setup, include=FALSE}
library(flexdashboard)
library(sf)
library(ggplot2)
library(tmap)
library(tmaptools)
library(leaflet)
library(tidyverse)
library(plotly)
library(hrbrthemes)
library(kableExtra)
library(DT)
```
```{r directory, echo=FALSE, message=FALSE}
setwd("E:/ADI BUANA/SEMESTER 7/PETA JATIM 2017 AKB")
mydata <- readr::read_csv("infant_jatim.csv")
map <- st_read("E:/ADI BUANA/SEMESTER 7/PETA JATIM 2017 AKB/infant.shp", stringsAsFactors = FALSE)
df = read.csv("E:\\ADI BUANA\\project\\jurnal\\infant.csv", sep = ";")
```
```{r import, echo=FALSE, warning=FALSE}
map_dan_data <- merge(map,mydata)
```
```{r}
mycolors <- c("blue", "#FFC125", "darkgreen", "darkorange")
```
Interactive Data Visualization
=====================================
Row
-------------------------------------
### **IMR**
```{r}
gauge(mydata$IMR,
min = 0,
max = 57,
symbol = "%",
gaugeSectors(success = c(0, 20),
warning = c(21, 40),
danger = c(41, 57),
colors = c("green", "yellow", "red")))
```
### Total Health Facility in East Java
```{r}
valueBox(sum(mydata$PUSKEMAS),
icon = 'fa-hospital-o')
```
### Highest IMR East Java Province
```{r}
valueBox(max(mydata$IMR),
icon = 'fa-area-chart')
```
### Highest Poverty East Java
```{r}
valueBox(max(mydata$MISKIN),
icon = 'fa-area-chart')
```
Row
-------------------------------------
### Top IMR East Java
```{r}
g1 <- mydata %>%
group_by(NAMA_KAB) %>%
filter(IMR > 45) %>%
plot_ly(labels = ~NAMA_KAB,
values = ~IMR,
marker = list(colors = mycolors)) %>%
add_pie(hole = 0.4) %>%
layout(xaxis = list(zeroline = F,
showline = F,
showticklabels = F,
showgrid = F),
yaxis = list(zeroline = F,
showline = F,
showticklabels=F,
showgrid=F))
g1
```
### Top Poverty East Java
```{r}
g2 <- mydata %>%
group_by(NAMA_KAB) %>%
filter(MISKIN > 16) %>%
plot_ly(labels = ~NAMA_KAB,
values = ~MISKIN,
marker = list(colors = mycolors)) %>%
add_pie(hole = 0.4) %>%
layout(xaxis = list(zeroline = F,
showline = F,
showticklabels = F,
showgrid = F),
yaxis = list(zeroline = F,
showline = F,
showticklabels=F,
showgrid=F))
g2
```
Scatter Plot
======================================
Row
--------------------------------------
### IMR Vs Poverty
```{r}
f1 <- plot_ly(mydata, x= ~ MISKIN) %>%
add_markers(y = ~ IMR,
text = ~paste("IMR: ", IMR),
showlegend = F) %>%
add_lines(y = ~fitted(loess(IMR ~ MISKIN)),
name = "Loess Smoother",
color = I("#FFC125"),
showlegend = T,
line = list(width=3)) %>%
layout(xaxis = list(title = "Poverty"),
yaxis = list(title = "IMR"))
f1
```
### IMR Vs Health Facility
```{r}
f2 <- plot_ly(mydata, x= ~ PUSKEMAS) %>%
add_markers( y = ~ IMR,
text = ~paste("IMR: ", IMR),
showlegend = F) %>%
add_lines(y = ~fitted(loess(IMR ~ PUSKEMAS)),
name = "Loess Smoother",
color = I("green"),
showlegend = T,
line = list(width=3)) %>%
layout(xaxis = list(title = "Health Facility"),
yaxis = list(title = "IMR"))
f2
```
IMR East Java
=======================================
Row
---------------------------------------
### IMR East Java by City and Regency
```{r}
p1 <- mydata %>%
filter(!is.na(IMR)) %>%
arrange(as.ordered(IMR)) %>%
mutate(NAMA_KAB = factor(NAMA_KAB, NAMA_KAB)) %>%
ggplot(aes(x = NAMA_KAB, y = IMR)) +
labs (title = "Infant Mortality Rate East Java" , x = " ", y = " ", subtitle = " IMR East Java 2016 ", caption = "Source : Central Bureau Statistic | Plot generate by : Antonito HC" ) +
geom_bar(stat = "identity", fill = "brown") +
coord_flip() +
theme_ipsum()
ggplotly(p1)
```
### IMR Indonesia by Province
```{r}
p2 <- df %>%
filter(!is.na(IMR)) %>%
arrange(as.ordered(IMR)) %>%
mutate(Provinsi = factor(Provinsi, Provinsi)) %>%
ggplot(aes(x = Provinsi, y = IMR)) +
labs (title = "Infant Mortality Rate Indonesia" ,
x = " ", y = " % IMR ", subtitle = " IMR Indonesia 2016 ", caption = "Source : Central Bureau Statistic | Plot generate by : Antonito HC" ) +
geom_bar(stat = "identity", fill = "red") +
coord_flip() +
theme_ipsum()
ggplotly(p2)
```
Map IMR
=====================================
Row
-------------------------------------
### IMR East Java
```{r}
tm_shape(map_dan_data) + tm_fill("IMR", id = "NAMA_KAB", style = "quantile", n = 4, palette = "Accent") + tm_borders(alpha = .6) + tm_layout(title = "East Java, Indonesia", legend.text.size = 1.1, legend.title.size = 1.4, legend.position = c("right", "top"), frame = FALSE)
```
```{r}
tmap_mode("view")
```
### Interactive Map IMR
```{r interactive, comment="", message=F, warning=F}
tmap_last()
```
Data Table
========================================
```{r}
datatable(mydata,
caption = "IMR 2016",
rownames = T,
filter = "top",
options = list(pageLength = 5))
```
Summary {data-orientation=columns}
===========================================
Column
-----------------------------------
### Highest IMR
```{r}
valueBox(max(mydata$IMR),
icon = "fa-area-chart" )
```
### Lowest IMR
```{r}
valueBox(min(mydata$IMR),
icon = "fa-area-chart" )
```
### Highest Poverty
```{r}
valueBox(max(mydata$MISKIN),
icon = "fa-area-chart" )
```
### Lowest Poverty
```{r}
valueBox(min(mydata$MISKIN),
icon = "fa-area-chart" )
```
### Total Health Facilities
```{r}
valueBox(sum(mydata$PUSKEMAS),
icon = "fa-building")
```
Column
---------------------------
Report
* The highest IMR in East Java was Probolinggo with `r max(mydata$IMR)` percent.
* The lowest IMR in East Java is Blitar City with `r min(mydata$IMR)` percect.
* The highest Poverty in East Java is Sampang with `r max(mydata$MISKIN)` percent.
* The lowest Poverty in East Java is Blitar City with `r min(mydata$MISKIN)` percent.
* The Total Health Facilities in East Java was `r sum(mydata$PUSKEMAS)`.
This report was generated on `r format(Sys.Date(), format = "%B %d, %Y")`.
About Report
========================================
Created by : Antonito (Majoring in Statistics and Minoring in Business Analytics [antowilcoxon@gmail.com] )
